Abstract
The performance of image restoration depends on the choice of image priors and regularization parameters. Recent studies reveal that heavy-tailed sparse gradient distributions of natural images can be modeled by hyper-Laplacian with the norm p∈ [0.5, 1]. However, it is still very challenging to determine the values of the norm and the regularization parameters. In this paper, we proposed a maximum a posterior (MAP) method for adaptive regularization parameters and norm selection. During the procedure of image restoration, by modeling the estimated residual and gradient distribution with Gaussian and hyper-Laplacian, respectively, we suggest a MAP formulation for the joint estimation of the latent image, regularization parameter, and norm. Then, we propose an alternating optimization method to iteratively solving the MAP problem. Experimental results show that, the proposed method obtained satisfactory restoration results for various degraded images with different noise level, and outperformed the other methods.
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Lin, X., Zhang, H., Deng, H., Zuo, W. (2013). Adaptive Regularization Parameters and Norm Selection for Sparse Gradient Based Image Restoration. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_99
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DOI: https://doi.org/10.1007/978-3-642-42057-3_99
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